def link(names, link_name, link_type='alt'):
'''
Simply adds metadata to the tplot variables, specifying which other tplot variables
contain other coordinates, typically positional information.
Parameters:
names: str or list of str
The names to link
link_name: str
The tplot variable to link to the names
link_type: str
The relationship that link_name has to the names. Values can be
lat, lon, alt, x, y, z, mso_x, mso_y, mso_z
'''
link_type = link_type.lower()
if not isinstance(names, list):
names = [names]
for i in names:
if i not in data_quants.keys():
print(str(i) + " is not currently in pytplot.")
return
if isinstance(i, int):
i = list(data_quants.keys())[i - 1]
data_quants[data_quants[i].
name].attrs['plot_options']['links'][link_type] = link_name
return
def tplot_save(names, filename=None):
"""
This function will save tplot variables into a single file by using the python "pickle" function.
This file can then be "restored" using tplot_restore. This is useful if you want to end the pytplot session,
but save all of your data/options. All variables and plot options can be read back into tplot with the
"tplot_restore" command.
Parameters:
names : str/list
A string or a list of strings of the tplot variables you would like saved.
filename : str, optional
The filename where you want to save the file.
Returns:
None
Examples:
>>> # Save a single tplot variable
>>> import pytplot
>>> x_data = [1,2,3,4,5]
>>> y_data = [1,2,3,4,5]
>>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data})
>>> pytplot.ylim('Variable1', 2, 4)
>>> pytplot.save('Variable1', filename='C:/temp/variable1.pytplot')
"""
if isinstance(names,int):
names = list(data_quants.keys())[names-1]
if not isinstance(names, list):
names = [names]
#Check that we have all available data
for name in names:
if isinstance(data_quants[name].data, list):
for data_name in data_quants[name].data:
if data_name not in names:
names.append(data_name)
#Pickle it up
to_pickle =[]
for name in names:
if name not in data_quants.keys():
print("That name is currently not in pytplot")
return
to_pickle.append(data_quants[name])
num_quants = len(to_pickle)
to_pickle = [num_quants] + to_pickle
temp_tplot_opt_glob = tplot_opt_glob
to_pickle.append(temp_tplot_opt_glob)
if filename==None:
filename='var_'+'-'.join(names)+'.pytplot'
pickle.dump(to_pickle, open(filename, "wb"))
return
def del_data(name=None):
"""
This function will delete tplot variables that are already stored in memory.
Parameters:
name : str
Name of the tplot variable to be deleted. If no name is provided, then
all tplot variables will be deleted.
Returns:
None
Examples:
>>> # Delete Variable 1
>>> import pytplot
>>> pytplot.del_data("Varaible1")
"""
if name is None:
tplot_names = list(data_quants.keys())
for i in tplot_names:
del data_quants[i]
return
if not isinstance(name, list):
name = [name]
entries = []
###
for i in name:
if ('?' in i) or ('*' in i):
for j in data_quants.keys():
var_verif = fnmatch.fnmatch(data_quants[j].name, i)
if var_verif == 1:
entries.append(data_quants[j].name)
else:
continue
for key in entries:
if key in data_quants:
del data_quants[key]
###
elif i not in data_quants.keys():
print(str(i) + " is currently not in pytplot.")
return
else:
temp_data_quants = data_quants[i]
str_name = temp_data_quants.name
del data_quants[str_name]
return
def get_ylimits(name, trg=None):
"""
This function will get extract the y-limits from the Tplot Variables stored in memory.
Parameters:
name : str
Name of the tplot variable
trg : list, optional
The time range that you would like to look in
Returns:
ymin : float
The minimum value of y
ymax : float
The maximum value of y
Examples:
>>> # Retrieve the y-limits from Variable 1
>>> import pytplot
>>> x_data = [1,2,3,4,5]
>>> y_data = [1,2,3,4,5]
>>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data})
>>> y1, y2 = pytplot.get_ylimits("Variable1")
"""
if isinstance(name, int):
name = list(data_quants.keys())[name - 1]
if not isinstance(name, list):
name = [name]
name_num = len(name)
ymin = None
ymax = None
for i in range(name_num):
if name[i] not in data_quants.keys():
print(str(name[i]) + " is currently not in pytplot.")
return
y = data_quants[name[i]]
# Slice the data around a time range
if trg is not None:
y = y.sel(time=slice(trg[0], trg[1]))
ymin = y.min(skipna=True)
ymax = y.max(skipna=False)
return ymin, ymax
def get_timespan(name):
"""
This function will get extract the time span from the Tplot Variables stored in memory.
Parameters:
name : str
Name of the tplot variable
Returns:
time_begin : float
The beginning of the time series
time_end : float
The end of the time series
Examples:
>>> # Retrieve the time span from Variable 1
>>> import pytplot
>>> x_data = [1,2,3,4,5]
>>> y_data = [1,2,3,4,5]
>>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data})
>>> time1, time2 = pytplot.get_timespan("Variable1")
"""
if name not in data_quants.keys():
print("That name is currently not in pytplot")
return
print("Start Time: " +
tplot_utilities.int_to_str(data_quants[name].trange[0]))
print("End Time: " +
tplot_utilities.int_to_str(data_quants[name].trange[1]))
return (data_quants[name].trange[0], data_quants[name].trange[1])
def get_data(name):
"""
This function will get extract the data from the Tplot Variables stored in memory.
Parameters:
name : str
Name of the tplot variable
Returns:
time_val : pandas dataframe index
data_val : list
Examples:
>>> # Retrieve the data from Variable 1
>>> import pytplot
>>> x_data = [1,2,3,4,5]
>>> y_data = [1,2,3,4,5]
>>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data})
>>> time, data = pytplot.get_data("Variable1")
"""
global data_quants
if name not in data_quants.keys():
print("That name is currently not in pytplot")
return
temp_data_quant = data_quants[name]
data_val = temp_data_quant.data.values
time_val = temp_data_quant.data.index
return (time_val, data_val)
def get_timespan(name):
"""
This function extracts the time span from the Tplot Variables stored in memory.
Parameters:
name : str
Name of the tplot variable
Returns:
time_begin : float
The beginning of the time series
time_end : float
The end of the time series
Examples:
>>> # Retrieve the time span from Variable 1
>>> import pytplot
>>> x_data = [1,2,3,4,5]
>>> y_data = [1,2,3,4,5]
>>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data})
>>> time1, time2 = pytplot.get_timespan("Variable1")
"""
if name not in data_quants.keys():
print("That name is currently not in pytplot")
return
return data_quants[name].attrs['plot_options']['trange'][0], data_quants[
name].attrs['plot_options']['trange'][1]
def zlim(name, min, max):
"""
This function will set the z axis range displayed for a specific tplot variable.
This is only used for spec plots, where the z axis represents the magnitude of the values
in each bin.
Parameters:
name : str
The name of the tplot variable that you wish to set z limits for.
min : flt
The start of the z axis.
max : flt
The end of the z axis.
Returns:
None
Examples:
>>> # Change the z range of Variable1
>>> import pytplot
>>> x_data = [1,2,3]
>>> y_data = [ [1,2,3] , [4,5,6], [7,8,9] ]
>>> v_data = [1,2,3]
>>> pytplot.store_data("Variable3", data={'x':x_data, 'y':y_data, 'v':v_data})
>>> pytplot.zlim('Variable1', 2, 3)
"""
if name not in data_quants.keys():
print("That name is currently not in pytplot.")
return
temp_data_quant = data_quants[name]
temp_data_quant.zaxis_opt['z_range'] = [min, max]
return
def ylim(name, min, max):
"""
This function will set the y axis range displayed for a specific tplot variable.
Parameters:
name : str
The name of the tplot variable that you wish to set y limits for.
min : flt
The start of the y axis.
max : flt
The end of the y axis.
Returns:
None
Examples:
>>> # Change the y range of Variable1
>>> import pytplot
>>> x_data = [1,2,3,4,5]
>>> y_data = [1,2,3,4,5]
>>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data})
>>> pytplot.ylim('Variable1', 2, 4)
"""
if name not in data_quants.keys():
print("That name is currently not in pytplot.")
return
temp_data_quant = data_quants[name]
temp_data_quant.yaxis_opt['y_range'] = [min, max]
return
def get_data(name, xarray=False):
"""
This function extracts the data from the tplot Variables stored in memory.
Parameters:
name : str
Name of the tplot variable
Returns: tuple of data/dimensions stored in pytplot
time_val : numpy array of seconds since 1970
data_val : n-dimensional array of data
spec_bins_val (if exists) : spectral bins if the plot is a spectrogram
v1_val (if exists) : numpy array of v1 dimension coordinates
v2_val {if exists} : numpy array of v2 dimension coordinates
v3_val (if exists) : numpy array of v3 dimension coordinates
Examples:
>>> # Retrieve the data from Variable 1
>>> import pytplot
>>> x_data = [1,2,3,4,5]
>>> y_data = [1,2,3,4,5]
>>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data})
>>> time, data = pytplot.get_data("Variable1")
"""
global data_quants
if name not in data_quants.keys():
print("That name is currently not in pytplot")
return
temp_data_quant = data_quants[name]
if xarray:
return temp_data_quant
if 'v1' in temp_data_quant.coords.keys(
) and 'v2' in temp_data_quant.coords.keys(
) and 'v3' in temp_data_quant.coords.keys():
return (temp_data_quant.time.values, temp_data_quant.data,
temp_data_quant.coords['v1'].values,
temp_data_quant.coords['v2'].values,
temp_data_quant.coords['v3'].values)
elif 'v1' in temp_data_quant.coords.keys(
) and 'v2' in temp_data_quant.coords.keys():
return (temp_data_quant.time.values, temp_data_quant.data,
temp_data_quant.coords['v1'].values,
temp_data_quant.coords['v2'].values)
elif 'v1' in temp_data_quant.coords.keys():
return (temp_data_quant.time.values, temp_data_quant.data,
temp_data_quant.coords['v1'].values)
elif 'v' in temp_data_quant.coords.keys():
return (temp_data_quant.time.values, temp_data_quant.data,
temp_data_quant.coords['v'].values)
elif 'spec_bins' in temp_data_quant.coords.keys():
return (temp_data_quant.time.values, temp_data_quant.data,
temp_data_quant.coords['spec_bins'].values)
return (temp_data_quant.time.values, temp_data_quant.data)
def link(names, link_name, link_type='alt'):
link_type = link_type.lower()
if not isinstance(names, list):
names = [names]
for i in names:
if i not in data_quants.keys():
print(str(i) + " is currently not in pytplot.")
return
if isinstance(i, int):
i = list(data_quants.keys())[i - 1]
data_quants[i].link_to_tvar(link_type, link_name)
return
def del_data(name=None):
"""
This function will delete tplot variables that are already stored in memory.
Parameters:
name : str
Name of the tplot variable to be deleted. If no name is provided, then
all tplot variables will be deleted.
Returns:
None
Examples:
>>> # Delete Variable 1
>>> import pytplot
>>> pytplot.del_data("Varaible1")
"""
if name is None:
tplot_names = list(data_quants.keys())
for i in tplot_names:
del data_quants[i]
return
if not isinstance(name, list):
name = [name]
for i in name:
if i not in data_quants.keys():
print(str(i) + " is currently not in pytplot.")
return
temp_data_quants = data_quants[i]
str_name = temp_data_quants.name
del data_quants[str_name]
return
def get_ylimits(name, trg = None):
"""
This function will get extract the y limites from the Tplot Variables stored in memory.
Parameters:
name : str
Name of the tplot variable
trg : list, optional
The time range that you would like to look in
Returns:
ymin : float
The minimum value of y
ymax : float
The maximum value of y
Examples:
>>> # Retrieve the y limits from Variable 1
>>> import pytplot
>>> x_data = [1,2,3,4,5]
>>> y_data = [1,2,3,4,5]
>>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data})
>>> y1, y2 = pytplot.get_ylimits("Variable1")
"""
if isinstance(name,int):
name = list(data_quants.keys())[name-1]
if not isinstance(name, list):
name = [name]
if not isinstance(name, list):
name = [name]
name_num = len(name)
ymin = None
ymax = None
for i in range(name_num):
if name[i] not in data_quants.keys():
print(str(name[i]) + " is currently not in pytplot.")
return
temp_data_quant = data_quants[name[i]]
yother = temp_data_quant.data
if trg is not None:
for column_name in yother.columns:
y = yother[column_name]
trunc_tempt_data_quant = y.truncate(before = trg[0], after = trg[1])
loc_min = trunc_tempt_data_quant.min(skipna=True)
loc_max = trunc_tempt_data_quant.max(skipna=True)
if (ymin is None) or (loc_min < ymin):
ymin = loc_min
if (ymax is None) or (loc_max > ymax):
ymax = loc_max
else:
for column_name in yother.columns:
y = yother[column_name]
loc_min = y.min(skipna=True)
loc_max = y.max(skipna=False)
if (ymin is None) or (loc_min < ymin):
ymin = loc_min
if (ymax is None) or (loc_max > ymax):
ymax = loc_max
print("Y Minimum: " + str(ymin))
print("Y Maximum: " + str(ymax))
return(ymin, ymax)
def timebar(t,
varname=None,
databar=False,
delete=False,
color='black',
thick=1,
dash=False):
"""
This function will add a vertical bar to all time series plots. This is useful if you
want to bring attention to a specific time.
Parameters:
t : flt/list
The time in seconds since Jan 01 1970 to place the vertical bar. If a list of numbers are supplied,
multiple bars will be created. If "databar" is set, then "t" becomes the point on the y axis to
place a horizontal bar.
varname : str/list, optional
The variable(s) to add the vertical bar to. If not set, the default is to add it to all current plots.
databar : bool, optional
This will turn the timebar into a horizontal data bar. If this is set True, then variable "t" becomes
the point on the y axis to place a horizontal bar.
delete : bool, optional
If set to True, at lease one varname must be supplied. The timebar at point "t" for variable "varname"
will be removed.
color : str
The color of the bar
thick : int
The thickness of the bar
dash : bool
If set to True, the bar is dashed rather than solid
Returns:
None
Examples:
>>> # Place a green time bar at 2017-07-17 00:00:00
>>> import pytplot
>>> pytplot.timebar(1500249600, color='green')
>>> # Place a dashed data bar at 5500 on the y axis
>>> pytplot.timebar(5500, dashed=True, databar=True)
>>> Place 3 magenta time bars of thickness 5
at [2015-12-26 05:20:01, 2015-12-26 08:06:40, 2015-12-26 08:53:19]
for variable 'sgx' plot
>>> pytplot.timebar([1451107201,1451117200,1451119999],'sgx',color='m',thick=5)
"""
# make sure t entered is a list
if not isinstance(t, list):
t = [t]
# if entries in list not numerical, run str_to_int
if not isinstance(t[0], (int, float, complex)):
t1 = []
for time in t:
t1.append(tplot_utilities.str_to_int(time))
t = t1
dim = 'height'
if databar is True:
dim = 'width'
dash_pattern = 'solid'
if dash is True:
dash_pattern = 'dashed'
if delete is True:
tplot_utilities.timebar_delete(t, varname, dim)
return
#if no varname specified, add timebars to every plot
if varname is None:
num_bars = len(t)
for i in range(num_bars):
tbar = {}
tbar['location'] = t[i]
tbar['dimension'] = dim
tbar['line_color'] = pytplot.tplot_utilities.rgb_color(color)[0]
tbar['line_width'] = thick
tbar['line_dash'] = dash_pattern
for name in data_quants:
temp_data_quants = data_quants[name]
temp_data_quants.attrs['plot_options']['time_bar'].append(tbar)
#if varname specified
else:
if not isinstance(varname, list):
varname = [varname]
for j in varname:
if j not in data_quants.keys():
print(str(j) + "is currently not in pytplot")
else:
num_bars = len(t)
for i in range(num_bars):
tbar = {}
tbar['location'] = t[i]
tbar['dimension'] = dim
tbar['line_color'] = pytplot.tplot_utilities.rgb_color(
color)[0]
tbar['line_width'] = thick
tbar['line_dash'] = dash_pattern
temp_data_quants = data_quants[j]
temp_data_quants.attrs['plot_options']['time_bar'].append(
tbar)
return
def options(name, option=None, value=None, opt_dict=None):
"""
This function allows the user to set a large variety of options for individual plots.
Parameters:
name : str
Name or number of the tplot variable
option : str
The name of the option. See section below.
value : str/int/float/list
The value of the option. See section below.
dict : dict
This can be a dictionary of option:value pairs. Option and value
will not be needed if this dictionary item is supplied.
Options:
=================== ========== =====
Options Value type Notes
=================== ========== =====
Color str/list red, green, blue, etc. Also takes in RGB tuples, i.e. (0,255,0) for green
Colormap str/list https://matplotlib.org/examples/color/colormaps_reference.html.
Spec int 1 sets the Tplot Variable to spectrogram mode, 0 reverts.
Alt int 1 sets the Tplot Variable to altitude plot mode, 0 reverts.
Map int 1 sets the Tplot Variable to latitude/longitude mode, 0 reverts.
link list Allows a user to reference one tplot variable to another.
ylog int 1 sets the y axis to log scale, 0 reverts.
zlog int 1 sets the z axis to log scale, 0 reverts (spectrograms only).
legend_names list A list of strings that will be used to identify the lines.
xlog_slice bool Sets x axis on slice plot to log scale if True.
ylog bool Set y axis on main plot window to log scale if True.
ylog_slice bool Sets y axis on slice plot to log scale if True.
zlog bool Sets z axis on main plot window to log scale if True.
line_style str scatter (to make scatter plots), or solid_line, dot, dash, dash_dot, dash_dot_dot_dot, long_dash.
char_size int Defines character size for plot labels, etc.
name str The title of the plot.
panel_size flt Number between (0,1], representing the percent size of the plot.
basemap str Full path and name of a background image for "Map" plots.
alpha flt Number between [0,1], gives the transparancy of the plot lines.
thick flt Sets plot line width.
yrange flt list Two numbers that give the y axis range of the plot.
zrange flt list Two numbers that give the z axis range of the plot.
xrange_slice flt list Two numbers that give the x axis range of spectrogram slicing plots.
yrange_slice flt list Two numbers that give the y axis range of spectrogram slicing plots.
ytitle str Title shown on the y axis.
ztitle str Title shown on the z axis. Spec plots only.
ysubtitle str Subtitle shown on the y axis.
zsubtitle str Subtitle shown on the z axis. Spec plots only.
plotter str Allows a user to implement their own plotting script in place of the ones
herein.
crosshair_x str Title for x-axis crosshair.
crosshair_y str Title for y-axis crosshair.
crosshair_z str Title for z-axis crosshair.
static str Datetime string that gives desired time to plot y and z values from a spec
plot.
static_tavg str Datetime string that gives desired time-averaged y and z values to plot
from a spec plot.
t_average int Seconds around which the cursor is averaged when hovering over spectrogram
plots.
'spec_plot_dim' int If variable two dimensions, this sets which dimension the variable will have on
on the y axis. All other dimensions are summed into this one.
=================== ========== =====
Returns:
None
Examples:
>>> # Change the y range of Variable1
>>> import pytplot
>>> x_data = [1,2,3,4,5]
>>> y_data = [1,2,3,4,5]
>>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data})
>>> pytplot.options('Variable1', 'yrange', [2,4])
>>> # Change Variable1 to use a log scale
>>> pytplot.options('Variable1', 'ylog', 1)
"""
if isinstance(name, int):
name = list(pytplot.data_quants.keys())[name]
if opt_dict is None:
opt_dict = {option: value}
else:
if not isinstance(opt_dict, dict):
print("dict must be a dictionary object. Returning.")
return
if not isinstance(name, list):
name = [name]
for i in name:
for option, value in opt_dict.items():
# Lower case option for consistency
option = option.lower()
if i not in data_quants.keys():
print(str(i) + " is currently not in pytplot.")
return
if option == 'color':
if isinstance(value, list):
data_quants[i].attrs['plot_options']['extras'][
'line_color'] = value
else:
data_quants[i].attrs['plot_options']['extras'][
'line_color'] = [value]
if option == 'link':
if isinstance(value, list):
pytplot.link(i, value[1], value[0])
if option == 'colormap':
if isinstance(value, list):
data_quants[i].attrs['plot_options']['extras'][
'colormap'] = value
else:
data_quants[i].attrs['plot_options']['extras'][
'colormap'] = [value]
if option == 'spec':
_reset_plots(i)
if value:
if 'spec_bins' not in data_quants[i].coords:
print(
f"{i} does not contain coordinates for spectrogram plotting. Continuing..."
)
else:
data_quants[i].attrs['plot_options']['extras'][
'spec'] = value
data_quants[i].attrs['plot_options']['yaxis_opt'][
'y_range'] = utilities.get_y_range(data_quants[i])
else:
data_quants[i].attrs['plot_options']['extras'][
'spec'] = value
data_quants[i].attrs['plot_options']['yaxis_opt'][
'y_range'] = utilities.get_y_range(data_quants[i])
if option == 'alt':
_reset_plots(i)
data_quants[i].attrs['plot_options']['extras']['alt'] = value
if option == 'map':
_reset_plots(i)
data_quants[i].attrs['plot_options']['extras']['map'] = value
if option == 'legend_names':
data_quants[i].attrs['plot_options']['yaxis_opt'][
'legend_names'] = value
if option == 'xlog_slice':
if value:
data_quants[i].attrs['plot_options']['slice_xaxis_opt'][
'xi_axis_type'] = 'log'
else:
data_quants[i].attrs['plot_options']['slice_xaxis_opt'][
'xi_axis_type'] = 'linear'
if option == 'ylog':
negflag = 0 # _ylog_check(data_quants, value, i)
if negflag == 0 and value:
data_quants[i].attrs['plot_options']['yaxis_opt'][
'y_axis_type'] = 'log'
else:
data_quants[i].attrs['plot_options']['yaxis_opt'][
'y_axis_type'] = 'linear'
if option == 'ylog_slice':
if value:
data_quants[i].attrs['plot_options']['slice_yaxis_opt'][
'yi_axis_type'] = 'log'
else:
data_quants[i].attrs['plot_options']['slice_yaxis_opt'][
'yi_axis_type'] = 'linear'
if option == 'zlog':
negflag = _zlog_check(data_quants, value, i)
if negflag == 0:
data_quants[i].attrs['plot_options']['zaxis_opt'][
'z_axis_type'] = 'log'
else:
data_quants[i].attrs['plot_options']['zaxis_opt'][
'z_axis_type'] = 'linear'
if option == 'nodata':
data_quants[i].attrs['plot_options']['line_opt'][
'visible'] = value
if option == 'line_style':
to_be = []
if value == 0 or value == 'solid_line':
to_be = []
elif value == 1 or value == 'dot':
to_be = [2, 4]
elif value == 2 or value == 'dash':
to_be = [6]
elif value == 3 or value == 'dash_dot':
to_be = [6, 4, 2, 4]
elif value == 4 or value == 'dash_dot_dot_dot':
to_be = [6, 4, 2, 4, 2, 4, 2, 4]
elif value == 5 or value == 'long_dash':
to_be = [10]
else:
to_be = value
data_quants[i].attrs['plot_options']['line_opt'][
'line_style'] = to_be
if (value == 6 or value == 'none'):
data_quants[i].attrs['plot_options']['line_opt'][
'visible'] = False
if option == 'char_size':
data_quants[i].attrs['plot_options']['extras'][
'char_size'] = value
if option == 'name':
data_quants[i].attrs['plot_options']['line_opt'][
'name'] = value
if option == "panel_size":
if value > 1 or value <= 0:
print("Invalid value. Should be (0, 1]")
return
data_quants[i].attrs['plot_options']['extras'][
'panel_size'] = value
if option == 'basemap':
data_quants[i].attrs['plot_options']['extras'][
'basemap'] = value
if option == 'alpha':
if value > 1 or value < 0:
print("Invalid value. Should be [0, 1]")
return
data_quants[i].attrs['plot_options']['extras']['alpha'] = value
if option == 'thick':
data_quants[i].attrs['plot_options']['line_opt'][
'line_width'] = value
if option == 'yrange' or option == 'y_range':
data_quants[i].attrs['plot_options']['yaxis_opt'][
'y_range'] = [value[0], value[1]]
if option == 'zrange' or option == 'z_range':
data_quants[i].attrs['plot_options']['zaxis_opt'][
'z_range'] = [value[0], value[1]]
if option == 'xrange_slice':
data_quants[i].attrs['plot_options']['slice_xaxis_opt'][
'xi_range'] = [value[0], value[1]]
if option == 'yrange_slice':
data_quants[i].attrs['plot_options']['slice_yaxis_opt'][
'yi_range'] = [value[0], value[1]]
if option == 'xtitle':
data_quants[i].attrs['plot_options']['xaxis_opt'][
'axis_label'] = value
if option == 'ytitle':
data_quants[i].attrs['plot_options']['yaxis_opt'][
'axis_label'] = value
if option == 'ztitle':
data_quants[i].attrs['plot_options']['zaxis_opt'][
'axis_label'] = value
if option == 'xsubtitle':
data_quants[i].attrs['plot_options']['xaxis_opt'][
'axis_subtitle'] = value
if option == 'ysubtitle':
data_quants[i].attrs['plot_options']['yaxis_opt'][
'axis_subtitle'] = value
if option == 'zsubtitle':
data_quants[i].attrs['plot_options']['zaxis_opt'][
'axis_subtitle'] = value
if option == 'ybar':
data_quants[i].attrs['plot_options']['extras']['ybar'] = value
if option == 'ybar_color':
data_quants[i].attrs['plot_options']['extras']['ybar'] = value
if option == 'ybar_size':
data_quants[i].attrs['plot_options']['extras']['ysize'] = value
if option == 'plotter':
_reset_plots(i)
data_quants[i].attrs['plot_options']['extras'][
'plotter'] = value
if option == 'crosshair_x':
data_quants[i].attrs['plot_options']['xaxis_opt'][
'crosshair'] = value
if option == 'crosshair_y':
data_quants[i].attrs['plot_options']['yaxis_opt'][
'crosshair'] = value
if option == 'crosshair_z':
data_quants[i].attrs['plot_options']['zaxis_opt'][
'crosshair'] = value
if option == 'static':
data_quants[i].attrs['plot_options']['extras'][
'static'] = value
if option == 'static_tavg':
data_quants[i].attrs['plot_options']['extras'][
'static_tavg'] = [value[0], value[1]]
if option == 't_average':
data_quants[i].attrs['plot_options']['extras'][
't_average'] = value
if option == 'spec_plot_dim':
attr_dict = deepcopy(data_quants[i].attrs)
data_dict = {}
data_dict['x'] = data_quants[i].coords['time'].values
data_values = data_quants[i].values
if len(data_values.shape) <= 2:
pass
else:
data_dict['y'] = np.swapaxes(data_values, 2, value)
for c in data_quants[i].coords:
if c == 'time' or c == 'spec_bins':
continue
data_dict[c] = data_quants[i].coords[c].values
v2_values = data_quants[i].coords["v2"].values
data_dict['v2'] = data_dict['v' + str(value)]
data_dict['v' + str(value)] = v2_values
pytplot.store_data(i, data=data_dict)
data_quants[i].attrs = attr_dict
data_quants[i].attrs['plot_options']['yaxis_opt'][
'y_range'] = utilities.get_y_range(data_quants[i])
return
def cdf_to_tplot(filenames, varformat=None, get_support_data=False,
prefix='', suffix='', plot=False, merge=False):
"""
This function will automatically create tplot variables from CDF files.
.. note::
Variables must have an attribute named "VAR_TYPE". If the attribute entry
is "data" (or "support_data"), then they will be added as tplot variables.
Additionally, data variables should have attributes named "DEPEND_TIME" or
"DEPEND_0" that describes which variable is x axis. If the data is 2D,
then an attribute "DEPEND_1" must describe which variable contains the
secondary axis.
Parameters:
filenames : str/list of str
The file names and full paths of CDF files.
varformat : str
The file variable formats to load into tplot. Wildcard character
"*" is accepted. By default, all variables are loaded in.
get_support_data: bool
Data with an attribute "VAR_TYPE" with a value of "support_data"
will be loaded into tplot. By default, only loads in data with a
"VAR_TYPE" attribute of "data".
prefix: str
The tplot variable names will be given this prefix. By default,
no prefix is added.
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
plot: bool
The data is plotted immediately after being generated. All tplot
variables generated from this function will be on the same plot.
merge: bool
If True, then data from different cdf files will be merged into
a single pytplot variable.
Returns:
List of tplot variables created.
"""
stored_variables = []
global data_quants
if isinstance(filenames, str):
filenames = [filenames]
elif isinstance(filenames, list):
filenames = filenames
else:
print("Invalid filenames input.")
return stored_variables
var_type = ['data']
if varformat == None:
varformat = ".*"
if get_support_data:
var_type.append('support_data')
try:
varformat = varformat.replace("*", ".*")
var_regex = re.compile(varformat)
except:
print("Error reading the varformat.")
return
for filename in filenames:
cdf_file = cdflib.CDF(filename)
cdf_info = cdf_file.cdf_info()
all_cdf_variables = cdf_info['rVariables'] + cdf_info['zVariables']
# Find the data variables
for var in all_cdf_variables:
if not re.match(var_regex, var):
continue
var_atts = cdf_file.varattsget(var)
if 'VAR_TYPE' not in var_atts:
continue
if var_atts['VAR_TYPE'] in var_type:
var_properties = cdf_file.varinq(var)
if "DEPEND_TIME" in var_atts:
x_axis_var = var_atts["DEPEND_TIME"]
elif "DEPEND_0" in var_atts:
x_axis_var = var_atts["DEPEND_0"]
else:
print("Cannot find x axis.")
print("No attribute named DEPEND_TIME or DEPEND_0 in variable " + var)
continue
data_type_description = cdf_file.varinq(x_axis_var)['Data_Type_Description']
# Find data name and if it is already in stored variables
var_name = prefix + var + suffix
to_merge = False
if (var_name in data_quants.keys()) and (merge == True):
prev_data_quant = data_quants[var_name].data
to_merge = True
xdata = cdf_file.varget(x_axis_var)
if ('CDF_TIME' in data_type_description) or ('CDF_EPOCH' in data_type_description):
xdata = cdflib.cdfepoch.unixtime(xdata)
ydata = cdf_file.varget(var)
if ydata is None:
continue
if "FILLVAL" in var_atts:
if (var_properties['Data_Type_Description'] == 'CDF_FLOAT' or
var_properties['Data_Type_Description'] == 'CDF_REAL4' or
var_properties['Data_Type_Description'] == 'CDF_DOUBLE' or
var_properties['Data_Type_Description'] == 'CDF_REAL8'):
if ydata[ydata == var_atts["FILLVAL"]].size != 0:
ydata[ydata == var_atts["FILLVAL"]] = np.nan
tplot_data = {'x': xdata, 'y': ydata}
depend_1 = None
depend_2 = None
if "DEPEND_1" in var_atts:
if var_atts["DEPEND_1"] in all_cdf_variables:
depend_1 = cdf_file.varget(var_atts["DEPEND_1"])
if "DEPEND_2" in var_atts:
if var_atts["DEPEND_2"] in all_cdf_variables:
depend_2 = cdf_file.varget(var_atts["DEPEND_2"])
if (depend_1 is not None) and (depend_2 is not None):
tplot_data['v1'] = depend_1
tplot_data['v2'] = depend_2
elif depend_1 is not None:
tplot_data['v'] = depend_1
elif depend_2 is not None:
tplot_data['v'] = depend_2
store_data(var_name, data=tplot_data)
if var_name not in stored_variables:
stored_variables.append(var_name)
display_type = var_atts.get("DISPLAY_TYPE", "time_series")
scale_type = var_atts.get("SCALE_TYP", "linear")
if display_type == "spectrogram":
options(var, 'spec', 1)
if scale_type == 'log':
options(var, 'ylog', 1)
if to_merge:
cur_data_quant = data_quants[var_name].data
merged_data = [prev_data_quant, cur_data_quant]
data_quants[var_name].data = pd.concat(merged_data)
cdf_file.close() if hasattr(cdf_file, "close") else None
if plot:
tplot(stored_variables)
return stored_variables
def timebar(t,
varname=None,
databar=False,
delete=False,
color='black',
thick=1,
dash=False):
"""
This function will add a vertical bar to all time series plots. This is useful if you
want to bring attention to a specific time.
Parameters:
t : flt/list
The time in seconds since Jan 01 1970 to place the vertical bar. If a list of numbers are supplied,
multiple bars will be created. If "databar" is set, then "t" becomes the point on the y axis to
place a horizontal bar.
varname : str/list, optional
The variable(s) to add the vertical bar to. If not set, the default is to add it to all current plots.
databar : bool, optional
This will turn the timebar into a horizontal data bar. If this is set True, then variable "t" becomes
the point on the y axis to place a horizontal bar.
delete : bool, optional
If set to True, at lease one varname must be supplied. The timebar at point "t" for variable "varname"
will be removed.
color : str
The color of the bar
thick : int
The thickness of the bar
dash : bool
If set to True, the bar is dashed rather than solid
Returns:
None
Examples:
>>> # Place a green time bar at 2017-07-17 00:00:00
>>> import pytplot
>>> pytplot.timebar(1500249600, color='grean)
>>> # Place a dashed data bar at 5500 on the y axis
>>> pytplot.timebar(5500, dashed=True, databar=True)
"""
if not isinstance(t, (int, float, complex)):
t = tplot_utilities.str_to_int(t)
dim = 'height'
if databar is True:
dim = 'width'
dash_pattern = 'solid'
if dash is True:
dash_pattern = 'dashed'
# Convert single value to a list so we don't have to write code to deal with
# both single values and lists.
if not isinstance(t, list):
t = [t]
# Convert values to seconds by multiplying by 1000
if databar is False:
num_bars = len(t)
for j in range(num_bars):
t[j] *= 1000
if delete is True:
tplot_utilities.timebar_delete(t, varname, dim)
return
# If no varname specified, add timebars to every plot
if varname is None:
num_bars = len(t)
for i in range(num_bars):
tbar = {}
tbar['location'] = t[i]
tbar['dimension'] = dim
tbar['line_color'] = color
tbar['line_width'] = thick
tbar['line_dash'] = dash_pattern
for name in data_quants:
temp_data_quants = data_quants[name]
temp_data_quants.time_bar.append(tbar)
else:
if not isinstance(varname, list):
varname = [varname]
for j in varname:
if j not in data_quants.keys():
print(str(j) + "is currently not in pytplot")
else:
num_bars = len(t)
for i in range(num_bars):
tbar = Span(location=t[i],
dimension=dim,
line_color=color,
line_width=thick,
line_dash=dash_pattern)
temp_data_quants = data_quants[j]
temp_data_quants.time_bar.append(tbar)
return
def options(name, option, value):
"""
This function allows the user to set a large variety of options for individual plots.
Parameters:
name : str
Name of the tplot variable
option : str
The name of the option. See section below
value : str/int/float/list
The value of the option. See section below.
Options:
=================== ========== =====
Options Value type Notes
=================== ========== =====
Color str/list Red, Orange, Yellow, Green, Blue, etc.
Colormap str/list https://matplotlib.org/examples/color/colormaps_reference.html.
Spec int 1 sets the Tplot Variable to spectrogram mode, 0 reverts.
Alt int 1 sets the Tplot Variable to altitude plot mode, 0 reverts.
Map int 1 sets the Tplot Variable to latitude/longitude mode, 0 reverts.
link list Allows a user to reference one tplot variable to another.
ylog int 1 sets the y axis to log scale, 0 reverts.
zlog int 1 sets the z axis to log scale, 0 reverts (spectrograms only).
legend_names list A list of strings that will be used to identify the lines.
xlog_interactive bool Sets x axis on interactive plot to log scale if True.
ylog bool Set y axis on main plot window to log scale if True.
ylog_interactive bool Sets y axis on interactive plot to log scale if True.
zlog bool Sets z axis on main plot window to log scale if True.
line_style str scatter (to make scatter plots), or solid_line, dot, dash, dash_dot, dash_dot_dot_dot, long_dash.
char_size int Defines character size for plot labels, etc.
name str The title of the plot.
panel_size flt Number between (0,1], representing the percent size of the plot.
basemap str Full path and name of a background image for "Map" plots.
alpha flt Number between [0,1], gives the transparancy of the plot lines.
thick flt Sets plot line width.
yrange flt list Two numbers that give the y axis range of the plot.
zrange flt list Two numbers that give the z axis range of the plot.
xrange_interactive flt list Two numberes that give the x axis range of interactive plots.
yrange_interactive flt list Two numberes that give the y axis range of interactive plots.
ytitle str Title shown on the y axis.
ztitle str Title shown on the z axis. Spec plots only.
plotter str Allows a user to implement their own plotting script in place of the ones
herein.
crosshair_x str Title for x-axis crosshair.
crosshair_y str Title for y-axis crosshair.
crosshair_z str Title for z-axis crosshair.
static str Datetime string that gives desired time to plot y and z values from a spec
plot.
static_tavg str Datetime string that gives desired time-averaged y and z values to plot
from a spec plot.
t_average int Seconds around which the cursor is averaged when hovering over spectrogram
plots.
=================== ========== =====
Returns:
None
Examples:
>>> # Change the y range of Variable1
>>> import pytplot
>>> x_data = [1,2,3,4,5]
>>> y_data = [1,2,3,4,5]
>>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data})
>>> pytplot.options('Variable1', 'yrange', [2,4])
>>> # Change Variable1 to use a log scale
>>> pytplot.options('Variable1', 'ylog', 1)
"""
if not isinstance(name, list):
name = [name]
option = option.lower()
for i in name:
if i not in data_quants.keys():
print(str(i) + " is currently not in pytplot.")
return
if option == 'color':
if isinstance(value, list):
data_quants[i].attrs['plot_options']['extras']['line_color'] = value
else:
data_quants[i].attrs['plot_options']['extras']['line_color'] = [value]
if option == 'link':
if isinstance(value, list):
pytplot.link(i, value[1], value[0])
if option == 'colormap':
if isinstance(value, list):
data_quants[i].attrs['plot_options']['extras']['colormap'] = value
else:
data_quants[i].attrs['plot_options']['extras']['colormap'] = [value]
if option == 'spec':
_reset_plots(i)
data_quants[i].attrs['plot_options']['extras']['spec'] = value
if option == 'alt':
_reset_plots(i)
data_quants[i].attrs['plot_options']['extras']['alt'] = value
if option == 'map':
_reset_plots(i)
data_quants[i].attrs['plot_options']['extras']['map'] = value
if option == 'legend_names':
data_quants[i].attrs['plot_options']['yaxis_opt']['legend_names'] = value
if option == 'xlog_interactive':
if value:
data_quants[i].attrs['plot_options']['interactive_xaxis_opt']['xi_axis_type'] = 'log'
else:
data_quants[i].attrs['plot_options']['interactive_xaxis_opt']['xi_axis_type'] = 'linear'
if option == 'ylog':
negflag = 0 # _ylog_check(data_quants, value, i)
if negflag == 0 and value:
data_quants[i].attrs['plot_options']['yaxis_opt']['y_axis_type'] = 'log'
else:
data_quants[i].attrs['plot_options']['yaxis_opt']['y_axis_type'] = 'linear'
if option == 'ylog_interactive':
if value:
data_quants[i].attrs['plot_options']['interactive_yaxis_opt']['yi_axis_type'] = 'log'
else:
data_quants[i].attrs['plot_options']['interactive_yaxis_opt']['yi_axis_type'] = 'linear'
if option == 'zlog':
negflag = _zlog_check(data_quants, value, i)
if negflag == 0:
data_quants[i].attrs['plot_options']['zaxis_opt']['z_axis_type'] = 'log'
else:
data_quants[i].attrs['plot_options']['zaxis_opt']['z_axis_type'] = 'linear'
if option == 'nodata':
data_quants[i].attrs['plot_options']['line_opt']['visible'] = value
if option == 'line_style':
to_be = []
if value == 0 or value == 'solid_line':
to_be = []
elif value == 1 or value == 'dot':
to_be = [2, 4]
elif value == 2 or value == 'dash':
to_be = [6]
elif value == 3 or value == 'dash_dot':
to_be = [6, 4, 2, 4]
elif value == 4 or value == 'dash_dot_dot_dot':
to_be = [6, 4, 2, 4, 2, 4, 2, 4]
elif value == 5 or value == 'long_dash':
to_be = [10]
else:
to_be=value
data_quants[i].attrs['plot_options']['line_opt']['line_style'] = to_be
if(value == 6 or value == 'none'):
data_quants[i].attrs['plot_options']['line_opt']['visible'] = False
if option == 'char_size':
data_quants[i].attrs['plot_options']['extras']['char_size'] = value
if option == 'name':
data_quants[i].attrs['plot_options']['line_opt']['name'] = value
if option == "panel_size":
if value > 1 or value <= 0:
print("Invalid value. Should be (0, 1]")
return
data_quants[i].attrs['plot_options']['extras']['panel_size'] = value
if option == 'basemap':
data_quants[i].attrs['plot_options']['extras']['basemap'] = value
if option == 'alpha':
if value > 1 or value < 0:
print("Invalid value. Should be [0, 1]")
return
data_quants[i].attrs['plot_options']['extras']['alpha'] = value
if option == 'thick':
data_quants[i].attrs['plot_options']['line_opt']['line_width'] = value
if option == ('yrange' or 'y_range'):
data_quants[i].attrs['plot_options']['yaxis_opt']['y_range'] = [value[0], value[1]]
if option == ('zrange' or 'z_range'):
data_quants[i].attrs['plot_options']['zaxis_opt']['z_range'] = [value[0], value[1]]
if option == 'xrange_interactive':
data_quants[i].attrs['plot_options']['interactive_xaxis_opt']['xi_range'] = [value[0], value[1]]
if option == 'yrange_interactive':
data_quants[i].attrs['plot_options']['interactive_yaxis_opt']['yi_range'] = [value[0], value[1]]
if option == 'xtitle':
data_quants[i].attrs['plot_options']['xaxis_opt']['axis_label'] = value
if option == 'ytitle':
data_quants[i].attrs['plot_options']['yaxis_opt']['axis_label'] = value
if option == 'ztitle':
data_quants[i].attrs['plot_options']['zaxis_opt']['axis_label'] = value
if option == 'plotter':
_reset_plots(i)
data_quants[i].attrs['plot_options']['extras']['plotter'] = value
if option == 'crosshair_x':
data_quants[i].attrs['plot_options']['xaxis_opt']['crosshair'] = value
if option == 'crosshair_y':
data_quants[i].attrs['plot_options']['yaxis_opt']['crosshair'] = value
if option == 'crosshair_z':
data_quants[i].attrs['plot_options']['zaxis_opt']['crosshair'] = value
if option == 'static':
data_quants[i].attrs['plot_options']['extras']['static'] = value
if option == 'static_tavg':
data_quants[i].attrs['plot_options']['extras']['static_tavg'] = [value[0], value[1]]
if option == 't_average':
data_quants[i].attrs['plot_options']['extras']['t_average'] = value
return
def options(name, option, value):
"""
This function allows the user to set a large variety of options for individual plots.
Parameters:
name : str
Name of the tplot variable
option : str
The name of the option. See section below
value : str/int/float/list
The value of the option. See section below.
Options:
============ ========== =====
Options Value type Notes
============ ========== =====
Color str/list Red, Orange, Yellow, Green, Blue, etc
Colormap str/list https://matplotlib.org/examples/color/colormaps_reference.html
Spec int 1 sets the Tplot Variable to spectrogram mode, 0 reverts
Alt int 1 sets the Tplot Variable to altitude plot mode, 0 reverts
Map int 1 sets the Tplot Variable to latitude/longitude mode, 0 reverts
ylog int 1 sets the y axis to log scale, 0 reverts
zlog int 1 sets the z axis to log scale, 0 reverts (spectrograms only)
legend_names list A list of strings that will be used to identify the lines
line_style str solid_line, dot, dash, dash_dot, dash_dot_dot_dot, long_dash
name str The title of the plot
panel_size flt Number between (0,1], representing the percent size of the plot
basemap str Full path and name of a background image for "Map" plots
alpha flt Number between [0,1], gives the transparancy of the plot lines
yrange flt list Two numbers that give the y axis range of the plot
zrange flt list Two numbers that give the z axis range of the plot
ytitle str Title shown on the y axis
ztitle str Title shown on the z axis. Spec plots only.
============ ========== =====
Returns:
None
Examples:
>>> # Change the y range of Variable1
>>> import pytplot
>>> x_data = [1,2,3,4,5]
>>> y_data = [1,2,3,4,5]
>>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data})
>>> pytplot.options('Variable1', 'yrange', [2,4])
>>> # Change Variable1 to use a log scale
>>> pytplot.options('Variable1', 'ylog', 1)
>>> # Change the line color of Variable1
>>> pytplot.options('Variable1', 'ylog', 1)
"""
#if isinstance(name,int):
# name = tplot_common.data_quants.keys()[name]
if not isinstance(name, list):
name = [name]
option = option.lower()
for i in name:
if i not in data_quants.keys():
print(str(i) + " is currently not in pytplot.")
return
if option == 'color':
if isinstance(value, list):
data_quants[i].extras['line_color'] = value
else:
data_quants[i].extras['line_color'] = [value]
if option == 'link':
if isinstance(value, list):
data_quants[i].link_to_tvar(value[0], value[1])
if option == 'colormap':
if isinstance(value, list):
data_quants[i].extras['colormap'] = value
else:
data_quants[i].extras['colormap'] = [value]
if option == 'spec':
data_quants[i].extras['spec'] = value
if option == 'alt':
data_quants[i].extras['alt'] = value
if option == 'map':
data_quants[i].extras['map'] = value
if option == 'ylog':
negflag = 0
namedata = data_quants[i]
##check variable data
#if negative numbers, don't allow log setting
datasets = []
if isinstance(namedata.data, list):
for oplot_name in namedata.data:
datasets.append(data_quants[oplot_name])
else:
datasets.append(namedata)
for dataset in datasets:
if 'spec' not in dataset.extras:
for column in dataset.data:
if np.nanmin(dataset.data[column]) < 0:
print(
'Negative data is incompatible with log plotting.'
)
negflag = 1
break
else:
if dataset.extras['spec'] == 1:
for column in dataset.spec_bins:
if np.nanmin(dataset.spec_bins[column]) < 0:
print(
'Negative data is incompatible with log plotting.'
)
negflag = 1
break
if value == 1 and negflag == 0:
data_quants[i].yaxis_opt['y_axis_type'] = 'log'
else:
data_quants[i].yaxis_opt['y_axis_type'] = 'linear'
if option == 'legend_names':
data_quants[i].yaxis_opt['legend_names'] = value
if option == 'zlog':
negflag = 0
namedata = data_quants[i]
##check variable data
#if negative numbers, don't allow log setting
datasets = []
if isinstance(namedata.data, list):
for oplot_name in namedata.data:
datasets.append(data_quants[oplot_name])
else:
datasets.append(namedata)
for dataset in datasets:
if 'spec' in dataset.extras:
if dataset.extras['spec'] == 1:
negflag = 0
for column in dataset.data:
if np.nanmin(dataset.data[column]) < 0:
print(
'Negative data is incompatible with log plotting.'
)
negflag = 1
break
#verify there are no negative values
if negflag == 0 and value == 1:
data_quants[i].zaxis_opt['z_axis_type'] = 'log'
else:
data_quants[i].zaxis_opt['z_axis_type'] = 'linear'
else:
if value == 1:
data_quants[i].zaxis_opt['z_axis_type'] = 'log'
else:
data_quants[i].zaxis_opt['z_axis_type'] = 'linear'
else:
if value == 1:
data_quants[i].zaxis_opt['z_axis_type'] = 'log'
else:
data_quants[i].zaxis_opt['z_axis_type'] = 'linear'
if option == 'nodata':
data_quants[i].line_opt['visible'] = value
if option == 'line_style':
to_be = []
if value == 0 or value == 'solid_line':
to_be = []
elif value == 1 or value == 'dot':
to_be = [2, 4]
elif value == 2 or value == 'dash':
to_be = [6]
elif value == 3 or value == 'dash_dot':
to_be = [6, 4, 2, 4]
elif value == 4 or value == 'dash_dot_dot_dot':
to_be = [6, 4, 2, 4, 2, 4, 2, 4]
elif value == 5 or value == 'long_dash':
to_be = [10]
data_quants[i].line_opt['line_dash'] = to_be
if (value == 6 or value == 'none'):
data_quants[i].line_opt['visible'] = False
if option == 'name':
data_quants[i].line_opt['name'] = value
if option == "panel_size":
if value > 1 or value <= 0:
print("Invalid value. Should be (0, 1]")
return
data_quants[i].extras['panel_size'] = value
if option == 'basemap':
data_quants[i].extras['basemap'] = value
if option == 'alpha':
if value > 1 or value < 0:
print("Invalid value. Should be [0, 1]")
return
data_quants[i].extras['alpha'] = value
if option == 'thick':
data_quants[i].line_opt['line_width'] = value
if option == 'transparency':
alpha_val = value / 100
data_quants[i].line_opt['line_alpha'] = alpha_val
if option == ('yrange' or 'y_range'):
data_quants[i].yaxis_opt['y_range'] = [value[0], value[1]]
if option == ('zrange' or 'z_range'):
data_quants[i].zaxis_opt['z_range'] = [value[0], value[1]]
if option == 'ytitle':
data_quants[i].yaxis_opt['axis_label'] = value
if option == 'ztitle':
data_quants[i].zaxis_opt['axis_label'] = value
if option == 'plotter':
data_quants[i].extras['plotter'] = value
return
def netcdf_to_tplot(filenames, time ='', prefix='', suffix='', plot=False, merge=False):
'''
This function will automatically create tplot variables from CDF files.
Parameters:
filenames : str/list of str
The file names and full paths of netCDF files.
time: str
The name of the netCDF file's time variable.
prefix: str
The tplot variable names will be given this prefix. By default,
no prefix is added.
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
plot: bool
The data is plotted immediately after being generated. All tplot
variables generated from this function will be on the same plot.
By default, a plot is not created.
merge: bool
If True, then data from different netCDF files will be merged into
a single pytplot variable.
Returns:
List of tplot variables created.
Examples:
>>> #Create tplot variables from a GOES netCDF file
>>> import pytplot
>>> file = "/Users/user_name/goes_files/g15_epead_a16ew_1m_20171201_20171231.nc"
>>> pytplot.netcdf_to_tplot(file, prefix='mvn_')
>>> #Add a prefix, and plot immediately.
>>> import pytplot
>>> file = "/Users/user_name/goes_files/g15_epead_a16ew_1m_20171201_20171231.nc"
>>> pytplot.netcdf_to_tplot(file, prefix='goes_prefix_', plot=True)
'''
from netCDF4 import Dataset
stored_variables = []
global data_quants
if isinstance(filenames, str):
filenames = [filenames]
elif isinstance(filenames, list):
filenames = filenames
else:
print("Invalid filenames input.")
#return stored_variables
for filename in filenames:
# Read in file
file = Dataset(filename, "r+")
# Creating dictionary that will contain variables and their attributes
vars_and_atts = {}
for name, variable in file.variables.items():
vars_and_atts[name] = {}
for attrname in variable.ncattrs():
vars_and_atts[name][attrname] = getattr(variable, attrname)
# Filling in missing values for each variable with np.nan (if values are not already nan)
# and saving the masked variables to a new dictionary
masked_vars = {} # Dictionary containing properly masked variables
for var in vars_and_atts.keys():
reg_var = file.variables[var]
try:
var_fill_value = vars_and_atts[var]['missing_value']
if np.isnan(var_fill_value) != True:
# We want to force missing values to be nan so that plots don't look strange
var_mask = np.ma.masked_where(reg_var == np.float32(var_fill_value), reg_var)
var_filled = np.ma.filled(var_mask, np.nan)
masked_vars[var] = var_filled
elif np.isnan(var_fill_value) == True:
# missing values are already np.nan, don't need to do anything
var_filled = reg_var
masked_vars[var] = var_filled
except: # continue # Go to next iteration, this variable doesn't have data to mask (probably just a descriptor variable (i.e., 'base_time')
var_filled = reg_var
masked_vars[var] = var_filled
# Most files are from GOES data, which seems to usually have 'time_tag' in them that contain time information.
# There is an exception filter below that will allow a user to pick a different time variable if time_tag doesn't exist.
if time != '':
time_var = file[time]
unix_times = change_time_to_unix_time(time_var)
elif time == '':
time = input('Please enter time variable name. \nVariable list: {l}'.format(l=vars_and_atts.keys()))
while True:
if time not in vars_and_atts.keys():
# Making sure we input a valid response (i.e., the variable exists in the dataset), and also avoiding
# plotting a time variable against time.... because I don't even know what that would mean and uncover.
print('Not a valid variable name, please try again.')
continue
elif time in vars_and_atts.keys():
time_var = time
unix_times = change_time_to_unix_time(time_var)
for i,var in enumerate(file.variables):
# Here, we are making sure that the variables are time-based, otherwise we don't want to store them as tplot variables!
if 'record' in file[var].dimensions[0] or 'time' in file[var].dimensions[0]:
# Store the data now, as well as merge variables if that's desired
var_name = prefix + var + suffix
to_merge = False
if (var_name in data_quants.keys() and (merge == True)):
prev_data_quant = data_quants[var_name].data
to_merge = True
tplot_data = {'x': unix_times, 'y': masked_vars[var]}
store_data(var_name, tplot_data)
if var_name not in stored_variables:
stored_variables.append(var_name)
if to_merge == True:
cur_data_quant = data_quants[var_name].data
merged_data = [prev_data_quant, cur_data_quant]
data_quants[var_name].data = pd.concat(merged_data)
# If we are interested in seeing a quick plot of the variables, do it
if plot:
tplot(stored_variables)
else:
# If the variable isn't time-bound, we're going to look at the next variable
continue
return (stored_variables)
def cdf_to_tplot(filenames, varformat=None, get_support_data=False,
prefix='', suffix='', plot=False, merge=False,
center_measurement=False, notplot=False):
"""
This function will automatically create tplot variables from CDF files.
.. note::
Variables must have an attribute named "VAR_TYPE". If the attribute entry
is "data" (or "support_data"), then they will be added as tplot variables.
Additionally, data variables should have attributes named "DEPEND_TIME" or
"DEPEND_0" that describes which variable is x axis. If the data is 2D,
then an attribute "DEPEND_1" must describe which variable contains the
secondary axis.
Parameters:
filenames : str/list of str
The file names and full paths of CDF files.
varformat : str
The file variable formats to load into tplot. Wildcard character
"*" is accepted. By default, all variables are loaded in.
get_support_data: bool
Data with an attribute "VAR_TYPE" with a value of "support_data"
will be loaded into tplot. By default, only loads in data with a
"VAR_TYPE" attribute of "data".
prefix: str
The tplot variable names will be given this prefix. By default,
no prefix is added.
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
plot: bool
The data is plotted immediately after being generated. All tplot
variables generated from this function will be on the same plot.
merge: bool
If True, then data from different cdf files will be merged into
a single pytplot variable.
center_measurement: bool
If True, the CDF epoch variables are time-shifted to the middle
of the accumulation interval by their DELTA_PLUS_VAR and
DELTA_MINUS_VAR variable attributes
notplot: bool
If True, then data are returned in a hash table instead of
being stored in tplot variables (useful for debugging, and
access to multi-dimensional data products)
Returns:
List of tplot variables created (unless notplot keyword is used).
"""
stored_variables = []
epoch_cache = {}
output_table = {}
metadata = {}
data_quants = {}
if isinstance(filenames, str):
filenames = [filenames]
elif isinstance(filenames, list):
filenames = filenames
else:
print("Invalid filenames input.")
return stored_variables
var_type = ['data']
if varformat is None:
varformat = ".*"
if get_support_data:
var_type.append('support_data')
varformat = varformat.replace("*", ".*")
var_regex = re.compile(varformat)
for filename in filenames:
cdf_file = cdflib.CDF(filename)
cdf_info = cdf_file.cdf_info()
all_cdf_variables = cdf_info['rVariables'] + cdf_info['zVariables']
# Find the data variables
for var in all_cdf_variables:
if not re.match(var_regex, var):
continue
var_atts = cdf_file.varattsget(var)
if 'VAR_TYPE' not in var_atts:
continue
if var_atts['VAR_TYPE'] in var_type:
var_atts = cdf_file.varattsget(var)
var_properties = cdf_file.varinq(var)
if "DEPEND_TIME" in var_atts:
x_axis_var = var_atts["DEPEND_TIME"]
elif "DEPEND_0" in var_atts:
x_axis_var = var_atts["DEPEND_0"]
else:
if var_atts['VAR_TYPE'].lower() == 'data':
print("Cannot find x axis.")
print("No attribute named DEPEND_TIME or DEPEND_0 in \
variable " + var)
continue
data_type_description \
= cdf_file.varinq(x_axis_var)['Data_Type_Description']
# Find data name and if it is already in stored variables
var_name = prefix + var + suffix
if epoch_cache.get(filename+x_axis_var) is None:
delta_plus_var = 0.0
delta_minus_var = 0.0
delta_time = 0.0
xdata = cdf_file.varget(x_axis_var)
epoch_var_atts = cdf_file.varattsget(x_axis_var)
# check for DELTA_PLUS_VAR/DELTA_MINUS_VAR attributes
if center_measurement:
if 'DELTA_PLUS_VAR' in epoch_var_atts:
delta_plus_var = cdf_file.varget(epoch_var_atts['DELTA_PLUS_VAR'])
delta_plus_var_att = cdf_file.varattsget(epoch_var_atts['DELTA_PLUS_VAR'])
# check if a conversion to seconds is required
if 'SI_CONVERSION' in delta_plus_var_att:
si_conv = delta_plus_var_att['SI_CONVERSION']
delta_plus_var = delta_plus_var.astype(float)*np.float(si_conv.split('>')[0])
elif 'SI_CONV' in delta_plus_var_att:
si_conv = delta_plus_var_att['SI_CONV']
delta_plus_var = delta_plus_var.astype(float)*np.float(si_conv.split('>')[0])
if 'DELTA_MINUS_VAR' in epoch_var_atts:
delta_minus_var = cdf_file.varget(epoch_var_atts['DELTA_MINUS_VAR'])
delta_minus_var_att = cdf_file.varattsget(epoch_var_atts['DELTA_MINUS_VAR'])
# check if a conversion to seconds is required
if 'SI_CONVERSION' in delta_minus_var_att:
si_conv = delta_minus_var_att['SI_CONVERSION']
delta_minus_var = delta_minus_var.astype(float)*np.float(si_conv.split('>')[0])
elif 'SI_CONV' in delta_minus_var_att:
si_conv = delta_minus_var_att['SI_CONV']
delta_minus_var = delta_minus_var.astype(float)*np.float(si_conv.split('>')[0])
# sometimes these are specified as arrays
if isinstance(delta_plus_var, np.ndarray) and isinstance(delta_minus_var, np.ndarray):
delta_time = (delta_plus_var-delta_minus_var)/2.0
else: # and sometimes constants
if delta_plus_var != 0.0 or delta_minus_var != 0.0:
delta_time = (delta_plus_var-delta_minus_var)/2.0
if epoch_cache.get(filename + x_axis_var) is None:
if ('CDF_TIME' in data_type_description) or \
('CDF_EPOCH' in data_type_description):
xdata = cdfepoch.unixtime(xdata)
epoch_cache[filename+x_axis_var] = np.array(xdata)+delta_time
else:
xdata = epoch_cache[filename + x_axis_var]
try:
ydata = cdf_file.varget(var)
except:
continue
if ydata is None:
continue
if "FILLVAL" in var_atts:
if (var_properties['Data_Type_Description'] ==
'CDF_FLOAT' or
var_properties['Data_Type_Description'] ==
'CDF_REAL4' or
var_properties['Data_Type_Description'] ==
'CDF_DOUBLE' or
var_properties['Data_Type_Description'] ==
'CDF_REAL8'):
if ydata[ydata == var_atts["FILLVAL"]].size != 0:
ydata[ydata == var_atts["FILLVAL"]] = np.nan
tplot_data = {'x': xdata, 'y': ydata}
depend_1 = None
depend_2 = None
depend_3 = None
if "DEPEND_1" in var_atts:
if var_atts["DEPEND_1"] in all_cdf_variables:
depend_1 = np.array(cdf_file.varget(var_atts["DEPEND_1"]))
if "DEPEND_2" in var_atts:
if var_atts["DEPEND_2"] in all_cdf_variables:
depend_2 = np.array(cdf_file.varget(var_atts["DEPEND_2"]))
if "DEPEND_3" in var_atts:
if var_atts["DEPEND_3"] in all_cdf_variables:
depend_3 = np.array(cdf_file.varget(var_atts["DEPEND_3"]))
nontime_varying_depends = []
if depend_1 is not None and depend_2 is not None and depend_3 is not None:
tplot_data['v1'] = depend_1
tplot_data['v2'] = depend_2
tplot_data['v3'] = depend_3
if len(depend_1.shape) == 1:
nontime_varying_depends.append('v1')
if len(depend_2.shape) == 1:
nontime_varying_depends.append('v2')
if len(depend_3.shape) == 1:
nontime_varying_depends.append('v3')
elif depend_1 is not None and depend_2 is not None:
tplot_data['v1'] = depend_1
tplot_data['v2'] = depend_2
if len(depend_1.shape) == 1:
nontime_varying_depends.append('v1')
if len(depend_2.shape) == 1:
nontime_varying_depends.append('v2')
elif depend_1 is not None:
tplot_data['v'] = depend_1
if len(depend_1.shape) == 1:
nontime_varying_depends.append('v')
elif depend_2 is not None:
tplot_data['v'] = depend_2
if len(depend_2.shape) == 1:
nontime_varying_depends.append('v')
metadata[var_name] = {'display_type': var_atts.get("DISPLAY_TYPE", "time_series"),
'scale_type': var_atts.get("SCALE_TYP", "linear")}
if var_name not in output_table:
output_table[var_name] = tplot_data
else:
var_data = output_table[var_name]
for output_var in var_data:
if output_var not in nontime_varying_depends:
var_data[output_var] = np.concatenate((var_data[output_var], tplot_data[output_var]))
if notplot:
return output_table
for var_name in output_table.keys():
to_merge = False
if var_name in data_quants.keys() and merge:
prev_data_quant = data_quants[var_name]
to_merge = True
try:
store_data(var_name, data=output_table[var_name])
except ValueError:
continue
if var_name not in stored_variables:
stored_variables.append(var_name)
if metadata.get(var_name) is not None:
if metadata[var_name]['display_type'] == "spectrogram":
options(var_name, 'spec', 1)
if metadata[var_name]['scale_type'] == 'log':
options(var_name, 'ylog', 1)
if to_merge is True:
cur_data_quant = data_quants[var_name]
plot_options = copy.deepcopy(data_quants[var_name].attrs['plot_options'])
data_quants[var_name] = xr.concat([prev_data_quant, cur_data_quant], dim='time')
data_quants[var_name].attrs['plot_options'] = plot_options
if notplot:
return output_table
if plot:
tplot(stored_variables)
return stored_variables
def options(name, option, value):
"""
This function allows the user to set a large variety of options for individual plots.
Parameters:
name : str
Name of the tplot variable
option : str
The name of the option. See section below
value : str/int/float/list
The value of the option. See section below.
Options:
============ ========== =====
Options Value type Notes
============ ========== =====
Color str/list Red, Orange, Yellow, Green, Blue, etc
Colormap str/list https://matplotlib.org/examples/color/colormaps_reference.html
Spec int 1 sets the Tplot Variable to spectrogram mode, 0 reverts
Alt int 1 sets the Tplot Variable to altitude plot mode, 0 reverts
Map int 1 sets the Tplot Variable to latitude/longitude mode, 0 reverts
ylog int 1 sets the y axis to log scale, 0 reverts
zlog int 1 sets the z axis to log scale, 0 reverts (spectrograms only)
legend_names list A list of strings that will be used to identify the lines
line_style str solid_line, dot, dash, dash_dot, dash_dot_dot_dot, long_dash
name str The title of the plot
panel_size flt Number between (0,1], representing the percent size of the plot
basemap str Full path and name of a background image for "Map" plots
alpha flt Number between [0,1], gives the transparancy of the plot lines
yrange flt list Two numbers that give the y axis range of the plot
zrange flt list Two numbers that give the z axis range of the plot
ytitle str Title shown on the y axis
ztitle str Title shown on the z axis. Spec plots only.
============ ========== =====
Returns:
None
Examples:
>>> # Change the y range of Variable1
>>> import pytplot
>>> x_data = [1,2,3,4,5]
>>> y_data = [1,2,3,4,5]
>>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data})
>>> pytplot.options('Variable1', 'yrange', [2,4])
>>> # Change Variable1 to use a log scale
>>> pytplot.options('Variable1', 'ylog', 1)
>>> # Change the line color of Variable1
>>> pytplot.options('Variable1', 'ylog', 1)
"""
#if isinstance(name,int):
# name = tplot_common.data_quants.keys()[name]
if not isinstance(name, list):
name = [name]
option = option.lower()
for i in name:
if i not in data_quants.keys():
print(str(i) + " is currently not in pytplot.")
return
(new_yaxis_opt, new_zaxis_opt, new_line_opt, new_extras) = set_options(
option, value, data_quants[i].yaxis_opt, data_quants[i].zaxis_opt,
data_quants[i].line_opt, data_quants[i].extras)
data_quants[i].yaxis_opt = new_yaxis_opt
data_quants[i].zaxis_opt = new_zaxis_opt
data_quants[i].line_opt = new_line_opt
data_quants[i].extras = new_extras
return
